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Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions

Author

Listed:
  • Xu, Zhengmeng
  • Wang, Yujie
  • Feng, Xiaotong
  • Wang, Yilin
  • Li, Yanli
  • Lin, Hai

Abstract

This paper presents the Quantum Gramian Angular Field (QGAF) method, which integrates quantum computing with deep learning to enhance forecasting in time series analysis. This method effectively converts stock return time series data into a format compatible with Convolutional Neural Network (CNN) training. Our empirical tests, conducted on stock market data from China, Hong Kong, and the United States, demonstrate that QGAF significantly outperforms the traditional GAF approach in accuracy. These findings underscore the potential of combining quantum computing and deep learning in financial time series forecasting.

Suggested Citation

  • Xu, Zhengmeng & Wang, Yujie & Feng, Xiaotong & Wang, Yilin & Li, Yanli & Lin, Hai, 2024. "Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions," Finance Research Letters, Elsevier, vol. 67(PA).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324008705
    DOI: 10.1016/j.frl.2024.105840
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